R中的交互式图 [英] Interactive Plots in R
问题描述
使用plotly库，我在R中绘制了以下图:
Using the plotly library, I made the following plot in R:
library(dplyr)
library(ggplot2)
library(plotly)
set.seed(123)
df <- data.frame(var1 = rnorm(1000,10,10),
var2 = rnorm(1000,5,5))
df <- df %>% mutate(var3 = ifelse(var1 <= 5 & var2 <= 5, "a", ifelse(var1 <= 10 & var2 <= 10, "b", "c")))
plot = df %>%
ggplot() + geom_point(aes(x=var1, y= var2, color= var3))
ggplotly(plot)
这是一个简单的散点图-生成两个随机变量，然后根据某些条件(例如var1和var2是否在特定范围之间)确定点的颜色.
This is a simple scatter plot - two random variables are generated, and then the colors of the points are decided by some criteria (e.g. if var1 and var2 are between certain ranges).
从这里，我还可以汇总统计信息:
From here, I could also summary statistics:
df$var3 = as.factor(df$var3)
summary = df %>%
group_by(var3) %>%
summarize(Mean_var1 = mean(var1), Mean_var2 = mean(var2), count=n())
# A tibble: 3 x 4
var3 Mean_var1 Mean_var2 count
* <fct> <dbl> <dbl> <int>
1 a -1.70 0.946 158
2 b 4.68 4.94 260
3 c 15.8 6.49 582
我的问题:是否可以在此绘图中添加一些按钮，以允许用户根据自定义选择为点着色?例如.像这样的东西:
My question: is it possible to add some buttons to this plot which would allow the user to color the points based on custom choices? E.g. something like this :
现在，用户可以在所需的任何范围内键入内容-点的颜色会更改，并且会生成一些摘要统计信息.
Now, the user can type in any range they want - and the color of the points change, and the some summary statistics are generated.
有人可以告诉我如何在R中执行此操作吗?
Can someone please show me how to do this in R?
我有这个想法-首先，我将创建一个庞大的表，该表将创建"var1"的所有可能范围组合.和"var2":
I had this idea - first I would create this massive table that would create all possible range combinations of "var1" and "var2":
vec1 <- c(-20:40,1)
vec2 <- c(-20:40,1)
a <- expand.grid(vec1, vec2)
for (i in seq_along(vec1)) {
for (j in seq_along(vec2)) {
df <- df %>% mutate(var3 = ifelse(var1 <= i & var2 <= i, "a", ifelse(var1 <= j & j <= 10, "b", "c")))
}
}
然后，根据用户想要的范围-SQL样式语句从该大规模表中将与这些范围相对应的行隔离开来:
Then, depending on which ranges the user wants - an SQL style statement isolate the rows from this massive table corresponding to those ranges :
custom_df = df[df$var1 > -20 & df$var1 <10 & df$var1 > -20 & df$var2 <10 , ]
然后，将针对"custom_df"进行单个抓取.并且摘要统计信息也将记录为"custom_df":
Then, an individual grap would be made for "custom_df" and summary statistics would also be recorded for "custom_df":
summary = custom_df %>%
group_by(var3) %>%
summarize(Mean_var1 = mean(var1), Mean_var2 = mean(var2), count=n())
但是我不确定如何在R中整齐有效地做到这一点.
But I am not sure how to neatly and efficiently do this in R.
有人可以告诉我该怎么做吗?
Can someone please show me how to do this?
谢谢
推荐答案
我已经构建了一个闪亮的小型应用程序来执行您的大多数要求.根据您预定义的大型数据框 df ，用户可以定义以下内容:
I have built a small shiny app to perform most of your requirements. Based on your pre-defined large dataframe df, user can define the following:
- 选择变量
var1和var2的最小值和最大值. - 选择标准来定义变量
var3，该变量用于显示不同颜色的数据点.这是一个范围. - 将图另存为HTML文件.
- 汇总统计信息显示为表格.
- Choose the minimum and maximum value for variables
var1andvar2. - Choose criteria to define the variable
var3, which is used to display different colors of data points. This is a range now. - Save plot as a HTML file.
- Summary stats displayed as a table.
您可以定义其他选项，以向用户提供选择颜色的选项，依此类推.为此，也许您应该在Google上找到如何使用 scale_color_manual().
You can define further options to provide the user the option to choose color and so on. For that perhaps you should google on how to use scale_color_manual().
更新:添加了用户选项，可根据var1和var2范围值选择红色和绿色.
Update: Added user option to choose red and green color based on var1 and var2 range values.
library(shiny)
library(plotly)
library(dplyr)
library(DT)
### define a large df
set.seed(123)
df <- data.frame(var1 = rnorm(1000,10,10),
var2 = rnorm(1000,15,15))
ui <- fluidPage(
titlePanel(p("My First Test App", style = "color:red")),
sidebarLayout(
sidebarPanel(
p("Choose Variable limits"),
# Horizontal line ----
tags$hr(),
uiOutput("var1a"), uiOutput("var1b"),
uiOutput("var2a"), uiOutput("var2b"),
uiOutput("criteria")
),
mainPanel(
DTOutput("summary"), br(),
plotlyOutput("plot"),
br(), br(), br(),
uiOutput("saveplotbtn")
)
)
)
server <- function(input, output, session){
output$var1a <- renderUI({
tagList(
numericInput("var11", "Variable 1 min",
min = min(df$var1), max = max(df$var1), value = min(df$var1))
)
})
output$var1b <- renderUI({
if (is.null(input$var11)){
low1 <- min(df$var1)
}else low1 <- max(min(df$var1),input$var11) ## cannot be lower than var 1 minimum
tagList(
numericInput("var12", "Variable 1 max", min = low1, max = max(df$var1), value = max(df$var1))
)
})
output$var2a <- renderUI({
tagList(
numericInput("var21", "Variable 2 min",
min = min(df$var2), max = max(df$var2), value = min(df$var2))
)
})
output$var2b <- renderUI({
if (is.null(input$var21)){
low2 <- min(df$var2)
}else low2 <- max(min(df$var2),input$var21) ## cannot be lower than var 2 minimum
tagList(
numericInput("var22", "Variable 2 max", min = low2, max = max(df$var2), value = max(df$var2))
)
})
output$criteria <- renderUI({
req(input$var11,input$var12,input$var21,input$var22)
tagList(
sliderInput("crit11", "Variable 1 red color range:",
min = -10, max = 0, value = c(-10,0)),
sliderInput("crit12", "Variable 2 red color range:",
min = -25, max = 0, value = c(-25,0)),
sliderInput("crit21", "Variable 1 green color range:",
min = 0.1, max = 10, value = c(0.1,10)),
sliderInput("crit22", "Variable 2 green color range:",
min = 0.1, max = 20, value = c(0.1,20))
)
})
dat <- reactive({
req(input$crit11,input$crit12,input$crit21,input$crit22)
df <- df %>% filter(between(var1, input$var11, input$var12)) %>%
filter(between(var2, input$var21, input$var22))
# df1 <- df %>% mutate(var3 = ifelse(var1 <= i & var2 <= i, "a", ifelse(var1 <= j & var2 <= j , "b", "c")))
df1 <- df %>% mutate(var3 = ifelse(between(var1, input$crit11[1], input$crit11[2]) & between(var2, input$crit12[1], input$crit12[2]), "a",
ifelse(between(var1, input$crit21[1], input$crit21[2]) & between(var2, input$crit22[1], input$crit22[2]), "b", "c")))
})
summari <- reactive({
req(dat())
df1 <- dat()
df1$var3 = as.factor(df1$var3)
summary = df1 %>%
group_by(var3) %>%
dplyr::summarize(Mean_var1 = mean(var1), Mean_var2 = mean(var2), count=n())
})
output$summary <- renderDT(summari())
rv <- reactiveValues()
observe({
req(dat())
p <- ggplot(data=dat()) + geom_point(aes(x=var1, y= var2, color= var3))
pp <- ggplotly(p)
rv$plot <- pp
})
output$plot <- renderPlotly({
rv$plot
})
output$saveplotbtn <- renderUI({
div(style="display: block; padding: 5px 350px 5px 50px;",
downloadBttn("saveHTML",
HTML("HTML"),
style = "fill",
color = "default",
size = "lg",
block = TRUE,
no_outline = TRUE
) )
})
output$saveHTML <- downloadHandler(
filename = function() {
paste("myplot", Sys.Date(), ".html", sep = "")
},
content = function(file) {
htmlwidgets::saveWidget(as_widget(rv$plot), file, selfcontained = TRUE) ## self-contained
}
)
}
shinyApp(ui, server)
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